About us

We are a group of data scientists with interest in brains and, more general, biomedical research. Right at the moment, much of the research in the lab is about deep learning and its applications. However, we are now very much interested in causality and its links with machine learning.

Research

Making sense of data is possibly the biggest problem in Neuroscience and beyond. We build algorithms to analyze data. We also use theory as well as computational and neural modeling to understand how information is processed in the nervous system, explaining data obtained in collaboration with electrophysiologists and in psychophysical experiments. Lastly, we constrain and develop new technologies aimed at obtaining data about brains.

Our conceptual work addresses information processing in the nervous system from two angles: (1) By analyzing and explaining electrophysiological data, we study what neurons do. (2) By analyzing and explaining human behavior, we study what all these neurons do together. Much of our work looks at these questions from a normative or causal viewpoint, asking what problems the nervous system should be solving. This often means taking a Bayesian approach. Bayesian decision theory is the systematic way of calculating how the nervous system may make good decisions in the presence of uncertainty. Causal inference from observational data promises to be a key enabler for progress in science.

Lab Members

Our research group is remarkably interdisciplinary. Our interests span statistics, physics, biology, applied mathematics, molecular biology, metascience, cognitive science, and many other disciplines. Visit our people page to see more information on each person who works in the lab (publications, contact information, photos).